5. Predicting Brain Age from T2-FLAIR Captures White Matter Aging Associated with Cardiovascular Risks

COEX 

In brain age prediction (BAP) studies, machine learning, especially deep learning, is commonly used to estimate 'brain age' (BA). The brain age gap (BAG) is measured as the difference between predicted brain age and chronological age (CA) and offers a quantitative measure for assessing normal versus abnormal aging.
Various modalities of brain MRI data, including T1, T2-FLAIR, functional, and diffusion MRI, provide distinct features of brain structure and function that change with aging. T1 MRI is optimal for evaluating morphological changes in the gray matter (GM) and white matter (WM), while the visibility of lesions, such as white matter hyperintensities (WMH) or ischemic stroke lesions, is more pronounced on T2-FLAIR [8]. Recently, studies have employed T2-FLAIR to predict brain age trajectories from the whole WM volume [7] or WMH volumes [2]. However, they do not examine the relationship between the spatial distribution of WMH and aging. Given the distinctive association of cardiovascular diseases with WMHs in deep WM and periventricular WMHs [4], we aimed to examine brain age as predicted by the spatial distribution of WMH. We modeled medial surfaces generated at various depths from the WM-GM boundary to the ventricles and projected T2-FLAIR intensity values onto these medial surfaces. These values at different depth surfaces were then inputted into graph convolutional networks (GCN) to predict brain age. We hypothesize that the BAGs derived from T2-FLAIR signals sampled at various depth levels within WM represent WM-specific brain aging and will be associated with cardiovascular risks.